Utility of a Chemotherapy Toxicity Prediction Tool for Older Patients in a Community Setting
Bibliographic record
Abstract
Background: Expert groups have recommended incorporation of a geriatric assessment into clinical practice for older patients starting oncologic therapy. However, that practice is not standard primarily because of resource limitations. In the present study, we evaluated the effect on treatment decisions by oncologists in the community oncology setting of a brief geriatric assessment tool that estimates risk of toxicity. Methods: This prospective longitudinal study in 5 community oncology practices in British Columbia involved patients 70 years of age and older starting a new cytotoxic chemotherapy regimen. Clinical personnel completed a brief validated geriatric assessment tool-the Cancer and Aging Research Group chemotherapy toxicity tool (carg-tt)-that estimates the risk of grade 3 or greater toxicity in older patients. Physicians were asked if the carg-tt changed their treatment plan or prompted extra supports. Patients were followed to assess the incidence of toxicity during treatment. Results: The study enrolled 199 patients between July 2016 and February 2018. Mean age was 77 years. Treatment was palliative in 61.4% of the group. Compared with physician judgment, the carg-tt predicted higher rates of toxicity. In 5 patients, treatment was changed based on the carg-tt. In 38.5% of the patients, data from the tool prompted extra supports. Within the first 3 cycles of treatment, 21.3% of patients had experienced grade 3 or greater toxicity. Conclusions: This study demonstrates that use of a brief geriatric assessment tool is possible in a broad community oncology practice. The tool modified the oncologist's supportive care plan for a significant number of older patients undertaking cytotoxic chemotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".